COMBINING LC-OCD ANALYSIS WITH DESIGN-OF-EXPERIMENTS METHODS TO OPTIMIZE AN ADVANCED OXIDATION PROCESS FOR THE TREATMENT OF INDUSTRIAL WASTEWATER

Advanced oxidation (AO) is widely used as a pre-treatment and/or polishing step for the treatment of wastewater from industrial processes and the destruction of particular contaminants in water sources. It has a high treatment efficacy for many different compounds and thus is ideally suited as a treatment technology for specialized facilities that receive shipments of wastewater from networks of industrial, manufacturing, and commercial facilities. The primary challenge is how to optimize the process because bulk measurements of organic content (e.g. TOC) give no information about the specific composition and specialized advanced analytical techniques (e.g. LC-MS) are unsuitable due to the complex composition. In this study, a novel combination of design-of-experiments (DOE) methods and LC-OCD analysis was used with actual wastewater samples in order to optimize the AO treatment conditions in terms of chemical reagent concentrations, develop statistical models of the process, and identify potential mechanisms of COD removal. A significant variation in organic content removal was obtained over the range of conditions tested in the DOE method. For example, the percent removal of organic contaminants in the one wastewater sample varied from a low of 36% to a high of 82%. Most importantly, it was found that the treatment performance differed quite significantly for wastewater samples of different composition. The results presented in our study prove the need to dynamically optimize the AO treatment conditions for wastewater sources of different origins. Furthermore, by the application of the LC-OCD analysis a step-by-step mechanism of COD removal was postulated.